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Abstract

The adoption of cube-satellites (cubesats) by the space community has drastically lowered the cost of access to space and reduced the development lifecycle from the hundreds of millions of dollars spent on traditional decade-long programs. Rapid deployment and low cost are attractive features of cubesat-based imaging that are conducive to applications such as disaster response and monitoring. One proposed application is 3D surface modeling through a high revisit rate constellation of cubesat imagers. This work begins with the characterization of an existing design for a cubesat imager based on ground sampled distance (GSD), signal-to-noise ratio (SNR), and smear. From this characterization, an existing 3D workflow is applied to datasets that have been degraded within the regime of spatial resolutions and signal-to-noise ratios anticipated for the cubesat imager. The fidelity of resulting point clouds are assessed locally for both an urban and a natural scene. The height of a building and normals to its surfaces are calculated from the urban scene, while quarry depth estimates and rough volume estimates of a pile of rocks are produced from the natural scene. Though the reconstructed scene geometry and completeness of the scene suffer noticeably from the degraded imagery, results indicate that useful information can still be extracted using some of these techniques up to a simulated GSD of 2 meters.

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